Machine learning systems for predictive modeling and related methods

ABSTRACT

A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/388,047 Apr. 18, 2019, the entire disclosure of which is incorporatedby reference.

FIELD OF INVENTION

The field relates to machine learning systems for predictive modeling.The machine learning systems are used identify characteristicsassociated with data sets, where the characteristics have relationshipsto the data sets that are unknown and unpredictable without the use ofthe disclosed machine learning systems. The field further relates to theuse of such machine learning systems in high volume fulfillment centersthat process medical orders, where the machine learning systems generatepredictive models that determine whether a given medical order data sethas a characteristic of prior authorization (PA).

BACKGROUND OF THE DISCLOSURE

The relationship between data sets and certain data characteristics maybe difficult to predict. Notably, in the case of medical order datasets, the relationship between the medical orders and certaincharacteristics are not static over time, and therefore not predictablewith a static model. In other words, existing static data models cannotpredictably determine certain characteristics of medical orders overtime. The reason that static data models cannot determine thesecharacteristics is because conditions of medical orders changefrequently, altering the relationships between elements of medicalorders and related characteristics. Nevertheless, it is crucial thatmedical order processing systems determine some of these characteristicsas early as possible in the information cycle of processing the medicalorder.

In the context of prescription medical orders, one characteristic thatis particularly important is the determination of whether a medicalorder is prior authorized. Prior authorization (PA) is a characteristicthat indicates whether authorization is required from a health careinsurer (or payer) after a physician prescribes a drug for a patient.Determining whether a medical order requires PA plays an essential rolein accurate, effective, and timely processing of prescription medicalorders. As such, in medical order systems, determining whether a medicalorder requires PA is crucial for successful processing of orders. Inknown systems, determination of whether a medical order requires PAcannot be completed because PA information is typically not explicitlyprovided. Instead, estimates of whether a medical order requires PA canbe made based on other order characteristics prior to order processing.However, as noted, whether a medical order requires PA cannot bepredictably determined with static models. As a result, using knownsystems, medical order systems are faced with a fundamental dilemma—anunacceptable delay in the processing of the medical order or a risk ofimproper medical order processing.

Therefore, in existing medical prescription processing systems,incorrect or improper authorization rule models may be used, causing thesystems to create erroneous results and improper results. In someexamples, the systems can only be improved through the use of manualverification steps, reversal of orders, or cancellation of orders. Evenwith such improvements, the risk of erroneous and improper resultsremains.

As such, prescription processing systems capable of predicting whether amedical order requires prior authorization, prior to order processing,are desirable.

BRIEF SUMMARY OF THE INVENTION

In one aspect, a machine learning system for training a data model topredict data states is provided. In the example embodiment, the machinelearning system is configured to train a data model to predict whether amedical order requires prior authorization (“PA”) for medical orderswithin a medical order data set so that related systems may processincoming medical orders with PA requirement determinations as predictedby the data model. The machine learning system includes a first datawarehouse system. The first data warehouse system includes a firstprocessor and a first memory. The first data warehouse system furtherincludes one or more historical orders and one or more payer responses.Each of the historical orders is associated with one of the payerresponses. The machine learning system also includes a firstprescription processing system (alternatively described as aprescription processing system, or a prescription processing computingdevice) that is in communication with the first data warehouse system.The prescription processing system includes a second processor and asecond memory. Using the second processor, the first prescriptionprocessing system is configured to perform at least the following steps:(a) receive a first portion of the historical orders and a first portionof the payer responses; (b) apply at least one data balancing operationto the first portion of the historical orders and the first portion ofthe payer responses; (c) generate a data model of the first portion ofthe historical orders and the first portion of the payer responses,wherein the data model is substantially represented by a tree-structureincluding one or more leaves; (d) apply a predictive machine learningmodel to the data model to generate a trained predictor of a whether amedical order requires PA, associated with order data; (e) receive oneor more production orders; (f) apply the trained predictor to theproduction orders to determine whether the medical order requires PA foreach of the production orders; and (g) process the production orderswith each determination of whether the medical order requires PA.

In another aspect, a method for training a data model to predict datastates is provided. In the example embodiment, the method is used totrain a data model to predict whether a medical order requires priorauthorization (“PA”) for medical orders within a medical order data setso that the prescription processing system and related systems mayprocess incoming medical orders with PA requirement determinations aspredicted by the data model. The method is performed by a firstprescription processing system included within a machine learningsystem. In the example embodiment, the first prescription processingsystem is in communication with a data warehouse system that is includedwithin the machine learning system. The first data warehouse systemincludes one or more historical orders and one or more payer responses.Each of the historical orders is associated with one of the payerresponses. The prescription processing system includes a processor and amemory. The method performed by the prescription processing systemincludes at least the following steps: (a) receive a first portion ofthe historical orders and a first portion of the payer responses; (b)apply at least one data balancing operation to the first portion of thehistorical orders and the first portion of the payer responses; (c)generate a data model of the first portion of the historical orders andthe first portion of the payer responses, wherein the data model issubstantially represented by a tree-structure including one or moreleaves; (d) apply a predictive machine learning model to the data modelto generate a trained predictor of whether a medical order requires PA,associated with order data; (e) receive one or more production orders;(f) apply the trained predictor to the production orders to determinewhether the medical order requires PA for each of the production orders;and (g) process the production orders with each determination of whetherthe medical order requires PA.

In yet another aspect, a prescription processing system used fortraining a data model to predict data states is provided. In the exampleembodiment, the prescription processing system trains a data model topredict whether a medical order requires prior authorization (“PA”) formedical orders within a medical order data set so that the prescriptionprocessing system and related systems may process incoming medicalorders with PA requirement determinations as predicted by the datamodel. In the example embodiment, the prescription processing system isincluded within a machine learning system. The prescription processingsystem is in communication with a data warehouse system that is alsoincluded within the machine learning system. The data warehouse systemincludes one or more historical orders and one or more payer responses.Each of the historical orders is associated with one of the payerresponses. The prescription processing system includes a processor and amemory. The prescription processing system is configured to perform atleast the following steps: (a) receiving a first portion of thehistorical orders and a first portion of the payer responses; (b)applying at least one data balancing operation to the first portion ofthe historical orders and the first portion of the payer responses; (c)generating a data model of the first portion of the historical ordersand the first portion of the payer responses, wherein the data model issubstantially represented by a tree-structure including one or moreleaves; (d) applying a predictive machine learning model to the datamodel to generate a trained predictor of whether a medical orderrequires PA, associated with order data; (e) receiving one or moreproduction orders; (f) applying the trained predictor to the productionorders to determine whether the medical order requires PA for each ofthe production orders; and (g) processing the production orders witheach determination of whether the medical order requires PA.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be better understood, and features, aspects andadvantages other than those set forth above will become apparent whenconsideration is given to the following detailed description thereof.Such detailed description makes reference to the following drawings,wherein:

FIG. 1 is a functional block diagram of an example system including ahigh-volume pharmacy.

FIG. 2 is a functional block diagram of an example pharmacy fulfillmentdevice, which may be deployed within the system of FIG. 1 .

FIG. 3 is a functional block diagram of an example order processingdevice, which may be deployed within the system of FIG. 1 .

FIG. 4 is a functional block diagram of a machine learning system thatmay be deployed within the system of FIG. 1 .

FIG. 5 is a functional block diagram of an example computing device thatmay be used in the system architecture of FIG. 4 .

FIG. 6 is a flow diagram representing the analytics process from theperspective of the prescription processing system or prescriptionprocessing computing device shown in FIG. 5 .

FIG. 7 is a diagram of elements of one or more example computing devicesthat may be used in the system shown in FIGS. 1 and 4 .

In the drawings, reference numbers may be reused to identify similarand/or identical elements.

DETAILED DESCRIPTION

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the disclosure belongs. Although any methods andmaterials similar to or equivalent to those described herein can be usedin the practice or testing of the present disclosure, the preferredmethods and materials are described below.

As used herein, the term “feature selection” refers to the process ofselecting a subset of relevant features (e.g., variables or predictors)that are used in the machine learning system to define data models.Feature selection may alternatively be described as variable selection,attribute selection, or variable subset selection. The feature selectionprocess of the machine learning system described herein allows theprescription processing system to simplify models to make them easier tointerpret, reduce the time to train the systems, reduce overfitting,enhance generalization, and avoid problems in dynamic optimization.

As used herein, the term “random forest algorithm” refers to an ensemblelearning algorithm used for classification, regression and other tasks.The random forest algorithm operates by constructing one or more ofdecision trees at the time of training a machine, and outputting theclass that is the mode of the classes, or mean prediction of theindividual trees to determine the classification or regression,respectively.

As used herein, the term “nearest neighbor algorithm” (also known as“k-nearest neighbor algorithm”) refers to a non-parametric method usedfor classification and regression. In both cases, the input consists ofthe k closest training examples in the feature space. Like the randomforest algorithm, nearest neighbor algorithm may be used forclassification or regression and may determine a class membership orobject property value, respectively. Generally, the nearest neighboralgorithm involves assigning greater weight to the contributions of thenearer neighbors, so that the nearer (or proximate) neighbors contributemore to the average than the more distant ones. In one example, eachneighbor is assigned a weight of 1/d, where d is the distance to theneighbor, and where the weight of the neighbor is thus inverse to thedistance from the neighbor.

As used herein, the term “naïve Bayesian algorithm” refers to aprobabilistic classifier or regression that utilizes Bayes's theorem andapplies naïve (or strong) independence assumptions between the features.Bayes's theorem can be stated as follows:

${{P\left( {A❘B} \right)} = \frac{{P\left( {B❘A} \right)}{P(A)}}{P(B)}},$where A and B are events and P(B)≠0. Like the random forest algorithmand the nearest neighbor algorithm, the naïve Bayesian algorithm may beused for classification or regression.

As used herein, the term “logistic regression algorithm” refers to amethod of estimating the parameters of a logistic model. A logisticmodel uses a logistic function to model at least one binary dependentvariable.

As used herein, the term “Principal Component Analysis” or “PCA” refersto a statistically based method of identifying features. Generally, PCAinvolves the use of orthogonal transformation to convert a set ofobservations of possibly correlated variables into a set of values oflinearly uncorrelated variables known as the “principal components”. Ifthere are n observations with p variables, then the number of distinctprincipal components is (n−1, p). This transformation is defined in sucha way that the first principal component has the largest possiblevariance (that is, accounts for as much of the variability in the dataas possible), and each succeeding component in turn has the highestvariance possible under the constraint that it is orthogonal to thepreceding components. The resulting vectors (each being a linearcombination of the variables and containing n observations) are anuncorrelated orthogonal basis set. PCA is sensitive to the relativescaling of the original variables.

The machine learning systems and methods described herein are configuredto address known technological problems confronting computing systemsand networks that process data sets, specifically the lack of knownstatic relationships between data sets and certain data characteristics.The machine learning systems and methods described are configured toaddress these known problems particularly as they relate to determiningwhether a medical order requires PA in medical order data sets. Asdescribed above, in existing medical prescription processing systems,incorrect or improper authorization rule models may be used, causing thesystems to create erroneous results and improper results because whethera medical order requires PA cannot be accurately determined. In someexamples, the systems can only be improved through the use of manualverification steps, reversal of orders, or cancellation of orders. Yet,even when these steps are taken, whether a medical order requires PAcannot be accurately and reliably determined.

The machine learning systems and methods described overcome knowndeficiencies in previous technological approaches. Using previousapproaches, static data models are typically rendered inaccurate whenchanges were made to payer rules and authorization rules. Such datamodels could also be rendered inaccurate when new categories of groupsand payers were added, when new drugs and drug categories were added,and when diagnosis codes (e.g., ICD10) were changed.

By contrast, the machine learning systems and methods provided allow theprescription processing system to adjust to changes in knownenvironmental conditions without requiring manual verification ofwhether a medical order requires PA. For example, in some situations,partially incorrect patient policy data or rejection codes may beprovided by payers. The machine learning systems and methods describedcan process medical order data containing such partially incorrect orincomplete data, and still return accurate determinations of whether amedical order requires PA. As such these machine learning systems andmethods solve a technological problem related to incomplete and/orunpredictable data (i.e., determinations of whether a medical orderrequires PA) that cannot be otherwise resolved using known methods andtechnologies. In particular, the proposed approach of machine learningusing a composite multi-algorithm approach to train a trained predictoris a significant technological improvement in the technological field ofdata sciences. Further, the proposed approach includes activere-training to ensure predictive accuracy. This approach also allows forre-training of the trained predictor as it is used. In this manner, thedisclosed machine learning methods and systems prevent the data modeland trained predictor from becoming static or stale and thereforepossibly prone to error.

Therefore, to overcome known problems of determining whether a medicalorder requires PA based on other prescription order characteristics, amachine learning system is provided. The machine learning system iscapable of performing predictive modeling of prescription ordercharacteristics. Once the machine learning system is trained, thepredictive models it generates accurately predict whether a medicalorder requires PA in incoming prescription orders. In the exampleembodiment, the machine learning system includes at least a prescriptionprocessing system and a data warehouse containing a prescription orderdatabase. The prescription processing system is configured tocommunicate with the data warehouse.

The prescription processing system receives one or more historicalorders (or historical medical claims) from the prescription orderdatabase included in the data warehouse. Each of the historical ordersincludes one or more data features. The data features may includedefined attributes including payer identifier, therapy identifier,prescription identifier, patient identifier, procedure identifier,diagnosis identifier, policy identifier, prescription group identifier,and drug identifier. The prescription processing system also receivesknown historical payer responses from the data warehouse. Historicalpayer responses include data points associated with previous payerresponses to medical orders including, without limitation, historicalorder numbers, historical order timestamp, historical order therapycode, historical order therapy identifier, historical order patientdiagnosis information, rejection codes, rejection amounts (ifapplicable), authorized amounts (if applicable), quantity dispensed,payment details (i.e., the amount that was paid for a given order), andpatient responsibility (if applicable). In some examples, PA requirementinformation may be partially available. In some examples, the datafeatures correspond to the historical payer responses. In otherexamples, the machine learning system is capable of processinghistorical payer responses that do not correspond (or do not fullycorrespond) to the historical payer responses.

In the example embodiment, the historical data is provided to theprescription processing system in batches on a staged basis, such thatthe prescription processing system learns from each batch. Specifically,the historical data is moved from a machine learning staging table to amachine learning target table. After the historical data is moved, theprescription processing system verifies that each record of thehistorical data does not already exist in the prescription processingsystem data records. The prescription processing system uses the machinelearning target table to learn from the verified unique data records.

The prescription processing system performs initial preprocessing on thehistorical orders to clean the data by, for example, removing erroneoushistorical orders and duplicative historical orders. The prescriptionprocessing system identifies duplicative historical orders by scanningthe historical orders with overlapping data features and overlappingtime stamps. The prescription processing system identifies erroneoushistorical records by comparing the values of certain data features toknown value options.

After preprocessing, the prescription processing system performs afeature selection (or feature extraction) on the historical orders tosample at least one of the data features for each of the historicalorders. The feature selection process may use a dimensionality reductionprocess known as Principal Component Analysis (“PCA”).

Alternatively, the feature selection process may use the random forestalgorithm to identify important features. A random forest is an ensemblemethod that uses a group of decision trees at the time that the machineis trained, and outputs the class that is most characteristic of theclasses.

The prescription processing system also performs a data balancingoperation on the historical orders. The data balancing operationinvolves adjusting the proportions of certain subsets of the historicaldata in order to train the machine learning data model. In one example,the data balancing operation incorporates distorting the historicalorders by selecting out a subset of historical orders with known PAstates. In another example, the data balancing operation incorporatesdistorting the historical orders by creating duplicates of the subset ofhistorical orders with unknown PA states. The prescription processingsystem also performs the data balancing operation by providing greaterweight to the lowest occurrences of data that are determined to besignificant to the model based on feature selection.

The prescription processing system trains a predictive machine learningmodel with the historical orders and the historical payer responses tomake a first prediction of the PA state. Specifically, the prescriptionprocessing system creates a data model based on the historical orders,and the historical payer responses, that defines a tree-structuredescriptive of the historical orders and further describing theassociated data features of the historical orders.

The prescription processing system trains the predictive machinelearning model using a combination of a) a nearest neighbor algorithmconfigured to consider the nearest twenty neighbors, b) a logisticregression algorithm, c) a random forest algorithm, and d) a naïveBayesian algorithm. This combination of algorithms provides unexpectedresults for predicting the PA state with a high degree of accuracy.

After the prescription processing system trains the predictive machinelearning model, it receives a second plurality of historical orders anda second set of historical payer responses. The prescription processingsystem applies the trained predictive machine learning model to thesecond plurality of historical orders to identify a predicted PArequirement associated with each of the second plurality of historicalorders. (In the example embodiment, the predicted PA requirement, andthe actual PA requirement, is either true or false.) The prescriptionprocessing system compares the predicted PA requirement determined bythe trained predictive machine learning model to the actual PArequirement indicated in the historical payer responses. Theprescription processing system identifies each discrepancy between apredicted PA requirement and the actual PA requirement for each of thesecond plurality of historical orders. The prescription processingsystem further determines an error rate for the trained predictivemachine learning model based on the identified discrepancies.

The prescription processing system receives a pre-defined threshold forPA requirement predictive accuracy. If the error rate exceeds thepre-defined threshold, the prescription processing system re-trains thepredictive machine learning model by training the trained predictivemachine learning model to the second plurality of historical orders andthe second set of historical payer responses. In such examples, theprescription processing system further receives a third plurality ofhistorical orders and a third set of historical payer responses andtests the re-trained predictive machine learning model using the thirdplurality of historical orders and the third set of historical payerresponses.

The prescription processing system iteratively re-trains the predictivemachine learning model until the trained (or re-trained) model has anerror rate that falls below the pre-defined threshold, and is thereforesufficiently accurate. The prescription processing system receivesproduction order data and makes determinations of whether a medicalorder requires PA using the trained (or re-trained) model. Theprescription processing system may also continue to iteratively re-trainthe predictive machine learning model on additional historical ordersand additional historical payer responses.

Generally, the systems and methods described herein are configured toperform at least the following steps: (a) receive a first portion of thehistorical orders and a first portion of the payer responses; (b) applyat least one data balancing operation to the first portion of thehistorical orders and the first portion of the payer responses; (c)generate a data model of the first portion of the historical orders andthe first portion of the payer responses, wherein the data model issubstantially represented by a tree-structure including one or moreleaves; (d) apply a predictive machine learning model to the data modelto generate a trained predictor of whether a medical order requires PA,associated with order data; (e) receive one or more production orders;(f) apply the trained predictor to the production orders to determinewhether a medical order requires PA for each of the production orders;(g) process the production orders with each associated determination ofwhether the medical order requires PA; (h) apply the predictive machinelearning model to the data model, wherein the predictive machinelearning model is trained using at least one of a) a nearest neighboralgorithm, b) a logistic regression algorithm, c) a random forestalgorithm, and d) a naïve Bayesian algorithm; (i) use the nearestneighbor algorithm to consider a set of neighbors including the nearesttwenty neighbors identified for each of the leaves of thetree-structure; (j) receive a second portion of the historical ordersand a second portion of the payer responses; (k) apply the trainedpredictor to the second portion of the historical orders to determinewhether a medical order requires PA for each of the second portion ofthe historical orders; (l) receive a pre-defined threshold for error;(m) determine an error rate for the trained predictor by comparing thedetermined PA requirement for each of the second portion of thehistorical orders to the associated second portion of the payerresponses; (n) determine whether the trained predictor requiresre-training; (o) upon determining that the trained predictor requiresre-training, re-train the trained predictor by generating a second datamodel of the second portion of the historical orders and the secondportion of the payer responses, wherein the second data model issubstantially represented by a tree-structure including one or moreleaves, and applying a predictive machine learning model to the seconddata model to generate a re-trained predictor of whether a medical orderrequires PA, associated with order data; (p) receive a third portion ofthe historical orders and a third portion of the payer responses; (q)apply the re-trained predictor to the third portion of the historicalorders to determine whether a medical order requires PA for each of thethird portion of the historical orders; (r) receive the pre-definedthreshold for error; (s) determine a second error rate for there-trained predictor by comparing the determined PA requirement for eachof the third portion of the historical orders to the associated thirdportion of the payer responses; (t) re-balance the first portion of thehistorical orders and the first portion of the payer responses; (u)receive additional historical orders and additional historical payerresponses; and (v) iteratively re-train the trained predictor on theadditional historical orders and the additional historical payerresponses in parallel with the use of the trained predictor.

FIG. 1 is a block diagram of an example implementation of a system 100for a high-volume pharmacy. While the system 100 is generally describedas being deployed in a high-volume pharmacy or a fulfillment center (forexample, a mail order pharmacy, a direct delivery pharmacy, etc.), thesystem 100 and/or components of the system 100 may otherwise be deployed(for example, in a lower-volume pharmacy, etc.). A high-volume pharmacymay be a pharmacy that is capable of filling at least some prescriptionsmechanically. The system 100 may include a benefit manager device 102and a pharmacy device 106 in communication with each other directlyand/or over a network 104.

The system 100 may also include one or more user device(s) 108. A user,such as a pharmacist, patient, data analyst, health plan administrator,etc., may access the benefit manager device 102 or the pharmacy device106 using the user device 108. The user device 108 may be a desktopcomputer, a laptop computer, a tablet, a smartphone, etc.

The benefit manager device 102 is a device operated by an entity that isat least partially responsible for creation and/or management of thepharmacy or drug benefit. While the entity operating the benefit managerdevice 102 is typically a pharmacy benefit manager (PBM), other entitiesmay operate the benefit manager device 102 on behalf of themselves orother entities (such as PBMs). For example, the benefit manager device102 may be operated by a health plan, a retail pharmacy chain, a drugwholesaler, a data analytics or other type of software-related company,etc. In some implementations, a PBM that provides the pharmacy benefitmay provide one or more additional benefits including a medical orhealth benefit, a dental benefit, a vision benefit, a wellness benefit,a radiology benefit, a pet care benefit, an insurance benefit, a longterm care benefit, a nursing home benefit, etc. The PBM may, in additionto its PBM operations, operate one or more pharmacies. The pharmaciesmay be retail pharmacies, mail order pharmacies, etc.

Some of the operations of the PBM that operates the benefit managerdevice 102 may include the following activities and processes. A member(or a person on behalf of the member) of a pharmacy benefit plan mayobtain a prescription drug at a retail pharmacy location (e.g., alocation of a physical store) from a pharmacist or a pharmacisttechnician. The member may also obtain the prescription drug throughmail order drug delivery from a mail order pharmacy location, such asthe system 100. In some implementations, the member may obtain theprescription drug directly or indirectly through the use of a machine,such as a kiosk, a vending unit, a mobile electronic device, or adifferent type of mechanical device, electrical device, electroniccommunication device, and/or computing device. Such a machine may befilled with the prescription drug in prescription packaging, which mayinclude multiple prescription components, by the system 100. Thepharmacy benefit plan is administered by or through the benefit managerdevice 102.

The member may have a copayment for the prescription drug that reflectsan amount of money that the member is responsible to pay the pharmacyfor the prescription drug. The money paid by the member to the pharmacymay come from, as examples, personal funds of the member, a healthsavings account (HSA) of the member or the member's family, a healthreimbursement arrangement (HRA) of the member or the member's family, ora flexible spending account (FSA) of the member or the member's family.In some instances, an employer of the member may directly or indirectlyfund or reimburse the member for the copayments.

The amount of the copayment required by the member may vary acrossdifferent pharmacy benefit plans having different plan sponsors orclients and/or for different prescription drugs. The member's copaymentmay be a flat copayment (in one example, $10), coinsurance (in oneexample, 10%), and/or a deductible (for example, responsibility for thefirst $500 of annual prescription drug expense, etc.) for certainprescription drugs, certain types and/or classes of prescription drugs,and/or all prescription drugs. The copayment may be stored in a storagedevice 110 or determined by the benefit manager device 102.

In some instances, the member may not pay the copayment or may only paya portion of the copayment for the prescription drug. For example, if ausual and customary cost for a generic version of a prescription drug is$4, and the member's flat copayment is $20 for the prescription drug,the member may only need to pay $4 to receive the prescription drug. Inanother example involving a worker's compensation claim, no copaymentmay be due by the member for the prescription drug.

In addition, copayments may also vary based on different deliverychannels for the prescription drug. For example, the copayment forreceiving the prescription drug from a mail order pharmacy location maybe less than the copayment for receiving the prescription drug from aretail pharmacy location.

In conjunction with receiving a copayment (if any) from the member anddispensing the prescription drug to the member, the pharmacy submits aclaim to the PBM for the prescription drug. After receiving the claim,the PBM (such as by using the benefit manager device 102) may performcertain adjudication operations including verifying eligibility for themember, identifying/reviewing an applicable formulary for the member todetermine any appropriate copayment, coinsurance, and deductible for theprescription drug, and performing a drug utilization review (DUR) forthe member. Further, the PBM may provide a response to the pharmacy (forexample, the pharmacy system 100) following performance of at least someof the aforementioned operations.

As part of the adjudication, a plan sponsor (or the PBM on behalf of theplan sponsor) ultimately reimburses the pharmacy for filling theprescription drug when the prescription drug was successfullyadjudicated. The aforementioned adjudication operations generally occurbefore the copayment is received and the prescription drug is dispensed.However in some instances, these operations may occur simultaneously,substantially simultaneously, or in a different order. In addition, moreor fewer adjudication operations may be performed as at least part ofthe adjudication process.

The amount of reimbursement paid to the pharmacy by a plan sponsorand/or money paid by the member may be determined at least partiallybased on types of pharmacy networks in which the pharmacy is included.In some implementations, the amount may also be determined based onother factors. For example, if the member pays the pharmacy for theprescription drug without using the prescription or drug benefitprovided by the PBM, the amount of money paid by the member may behigher than when the member uses the prescription or drug benefit. Insome implementations, the amount of money received by the pharmacy fordispensing the prescription drug and for the prescription drug itselfmay be higher than when the member uses the prescription or drugbenefit. Some or all of the foregoing operations may be performed byexecuting instructions stored in the benefit manager device 102 and/oran additional device.

Examples of the network 104 include a Global System for MobileCommunications (GSM) network, a code division multiple access (CDMA)network, 3rd Generation Partnership Project (3GPP), an Internet Protocol(IP) network, a Wireless Application Protocol (WAP) network, or an IEEE802.11 standards network, as well as various combinations of the abovenetworks. The network 104 may include an optical network. The network104 may be a local area network or a global communication network, suchas the Internet. In some implementations, the network 104 may include anetwork dedicated to prescription orders: a prescribing network such asthe electronic prescribing network operated by Surescripts of Arlington,Va.

Moreover, although the system shows a single network 104, multiplenetworks can be used. The multiple networks may communicate in seriesand/or parallel with each other to link the devices 102-110.

The pharmacy device 106 may be a device associated with a retailpharmacy location (e.g., an exclusive pharmacy location, a grocery storewith a retail pharmacy, or a general sales store with a retail pharmacy)or other type of pharmacy location at which a member attempts to obtaina prescription. The pharmacy may use the pharmacy device 106 to submitthe claim to the PBM for adjudication.

Additionally, in some implementations, the pharmacy device 106 mayenable information exchange between the pharmacy and the PBM. Forexample, this may allow the sharing of member information such as drughistory that may allow the pharmacy to better service a member (forexample, by providing more informed therapy consultation and druginteraction information). In some implementations, the benefit managerdevice 102 may track prescription drug fulfillment and/or otherinformation for users that are not members, or have not identifiedthemselves as members, at the time (or in conjunction with the time) inwhich they seek to have a prescription filled at a pharmacy.

The pharmacy device 106 may include a pharmacy fulfillment device 112,an order processing device 114, and a pharmacy management device 116 incommunication with each other directly and/or over the network 104. Theorder processing device 114 may receive information regarding fillingprescriptions and may direct an order component to one or more devicesof the pharmacy fulfillment device 112 at a pharmacy. The pharmacyfulfillment device 112 may fulfill, dispense, aggregate, and/or pack theorder components of the prescription drugs in accordance with one ormore prescription orders directed by the order processing device 114.

In general, the order processing device 114 is a device located withinor otherwise associated with the pharmacy to enable the pharmacyfulfilment device 112 to fulfill a prescription and dispenseprescription drugs. In some implementations, the order processing device114 may be an external order processing device separate from thepharmacy and in communication with other devices located within thepharmacy.

For example, the external order processing device may communicate withan internal pharmacy order processing device and/or other deviceslocated within the system 100. In some implementations, the externalorder processing device may have limited functionality (e.g., asoperated by a user requesting fulfillment of a prescription drug), whilethe internal pharmacy order processing device may have greaterfunctionality (e.g., as operated by a pharmacist).

The order processing device 114 may track the prescription order as itis fulfilled by the pharmacy fulfillment device 112. The prescriptionorder may include one or more prescription drugs to be filled by thepharmacy. The order processing device 114 may make pharmacy routingdecisions and/or order consolidation decisions for the particularprescription order. The pharmacy routing decisions include whatdevice(s) in the pharmacy are responsible for filling or otherwisehandling certain portions of the prescription order. The orderconsolidation decisions include whether portions of one prescriptionorder or multiple prescription orders should be shipped together for auser or a user family. The order processing device 114 may also trackand/or schedule literature or paperwork associated with eachprescription order or multiple prescription orders that are beingshipped together. In some implementations, the order processing device114 may operate in combination with the pharmacy management device 116.

The order processing device 114 may include circuitry, a processor, amemory to store data and instructions, and communication functionality.The order processing device 114 is dedicated to performing processes,methods, and/or instructions described in this application. Other typesof electronic devices may also be used that are specifically configuredto implement the processes, methods, and/or instructions described infurther detail below.

In some implementations, at least some functionality of the orderprocessing device 114 may be included in the pharmacy management device116. The order processing device 114 may be in a client-serverrelationship with the pharmacy management device 116, in a peer-to-peerrelationship with the pharmacy management device 116, or in a differenttype of relationship with the pharmacy management device 116. The orderprocessing device 114 and/or the pharmacy management device 116 maycommunicate directly (for example, such as by using a local storage)and/or through the network 104 (such as by using a cloud storageconfiguration, software as a service, etc.) with the storage device 110.

The storage device 110 may include: non-transitory storage (for example,memory, hard disk, CD-ROM, etc.) in communication with the benefitmanager device 102 and/or the pharmacy device 106 directly and/or overthe network 104. The non-transitory storage may store order data 118,member data 120, claims data 122, drug data 124, prescription data 126,and/or plan sponsor data 128. Further, the system 100 may includeadditional devices, which may communicate with each other directly orover the network 104.

The order data 118 may be related to a prescription order. The orderdata may include type of the prescription drug (for example, drug nameand strength) and quantity of the prescription drug. The order data 118may also include data used for completion of the prescription, such asprescription materials. In general, prescription materials include anelectronic copy of information regarding the prescription drug forinclusion with or otherwise in conjunction with the fulfilledprescription. The prescription materials may include electronicinformation regarding drug interaction warnings, recommended usage,possible side effects, expiration date, date of prescribing, etc. Theorder data 118 may be used by a high-volume fulfillment center tofulfill a pharmacy order.

In some implementations, the order data 118 includes verificationinformation associated with fulfillment of the prescription in thepharmacy. For example, the order data 118 may include videos and/orimages taken of (i) the prescription drug prior to dispensing, duringdispensing, and/or after dispensing, (ii) the prescription container(for example, a prescription container and sealing lid, prescriptionpackaging, etc.) used to contain the prescription drug prior todispensing, during dispensing, and/or after dispensing, (iii) thepackaging and/or packaging materials used to ship or otherwise deliverthe prescription drug prior to dispensing, during dispensing, and/orafter dispensing, and/or (iv) the fulfillment process within thepharmacy. Other types of verification information such as barcode dataread from pallets, bins, trays, or carts used to transport prescriptionswithin the pharmacy may also be stored as order data 118.

The member data 120 includes information regarding the membersassociated with the PBM. The information stored as member data 120 mayinclude personal information, personal health information, protectedhealth information, etc. Examples of the member data 120 include name,address, telephone number, e-mail address, prescription drug history,etc. The member data 120 may include a plan sponsor identifier thatidentifies the plan sponsor associated with the member and/or a memberidentifier that identifies the member to the plan sponsor. The memberdata 120 may include a member identifier that identifies the plansponsor associated with the user and/or a user identifier thatidentifies the user to the plan sponsor. The member data 120 may alsoinclude dispensation preferences such as type of label, type of cap,message preferences, language preferences, etc.

The member data 120 may be accessed by various devices in the pharmacy(for example, the high-volume fulfillment center, etc.) to obtaininformation used for fulfillment and shipping of prescription orders. Insome implementations, an external order processing device operated by oron behalf of a member may have access to at least a portion of themember data 120 for review, verification, or other purposes.

In some implementations, the member data 120 may include information forpersons who are users of the pharmacy but are not members in thepharmacy benefit plan being provided by the PBM. For example, theseusers may obtain drugs directly from the pharmacy, through a privatelabel service offered by the pharmacy, the high-volume fulfillmentcenter, or otherwise. In general, the use of the terms “member” and“user” may be used interchangeably.

The claims data 122 includes information regarding pharmacy claimsadjudicated by the PBM under a drug benefit program provided by the PBMfor one or more plan sponsors. In general, the claims data 122 includesan identification of the client that sponsors the drug benefit programunder which the claim is made, and/or the member that purchased theprescription drug giving rise to the claim, the prescription drug thatwas filled by the pharmacy (e.g., the national drug code number, etc.),the dispensing date, generic indicator, generic product identifier (GPI)number, medication class, the cost of the prescription drug providedunder the drug benefit program, the copayment/coinsurance amount, rebateinformation, and/or member eligibility, etc. Additional information maybe included.

In some implementations, other types of claims beyond prescription drugclaims may be stored in the claims data 122. For example, medicalclaims, dental claims, wellness claims, or other types ofhealth-care-related claims for members may be stored as a portion of theclaims data 122.

In some implementations, the claims data 122 includes claims thatidentify the members with whom the claims are associated. Additionallyor alternatively, the claims data 122 may include claims that have beende-identified (that is, associated with a unique identifier but not witha particular, identifiable member).

The drug data 124 may include drug name (e.g., technical name and/orcommon name), other names by which the drug is known, activeingredients, an image of the drug (such as in pill form), etc. The drugdata 124 may include information associated with a single medication ormultiple medications.

The prescription data 126 may include information regardingprescriptions that may be issued by prescribers on behalf of users, whomay be members of the pharmacy benefit plan—for example, to be filled bya pharmacy. Examples of the prescription data 126 include user names,medication or treatment (such as lab tests), dosing information, etc.The prescriptions may include electronic prescriptions or paperprescriptions that have been scanned. In some implementations, thedosing information reflects a frequency of use (e.g., once a day, twicea day, before each meal, etc.) and a duration of use (e.g., a few days,a week, a few weeks, a month, etc.).

In some implementations, the order data 118 may be linked to associatedmember data 120, claims data 122, drug data 124, and/or prescriptiondata 126.

The plan sponsor data 128 includes information regarding the plansponsors of the PBM. Examples of the plan sponsor data 128 includecompany name, company address, contact name, contact telephone number,contact e-mail address, etc.

FIG. 2 illustrates the pharmacy fulfillment device 112 according to anexample implementation. The pharmacy fulfillment device 112 may be usedto process and fulfill prescriptions and prescription orders. Afterfulfillment, the fulfilled prescriptions are packed for shipping.

The pharmacy fulfillment device 112 may include devices in communicationwith the benefit manager device 102, the order processing device 114,and/or the storage device 110, directly or over the network 104.Specifically, the pharmacy fulfillment device 112 may include palletsizing and pucking device(s) 206, loading device(s) 208, inspectdevice(s) 210, unit of use device(s) 212, automated dispensing device(s)214, manual fulfillment device(s) 216, review devices 218, imagingdevice(s) 220, cap device(s) 222, accumulation devices 224, packingdevice(s) 226, literature device(s) 228, unit of use packing device(s)230, and mail manifest device(s) 232. Further, the pharmacy fulfillmentdevice 112 may include additional devices, which may communicate witheach other directly or over the network 104.

In some implementations, operations performed by one of these devices206-232 may be performed sequentially, or in parallel with theoperations of another device as may be coordinated by the orderprocessing device 114. In some implementations, the order processingdevice 114 tracks a prescription with the pharmacy based on operationsperformed by one or more of the devices 206-232.

In some implementations, the pharmacy fulfillment device 112 maytransport prescription drug containers, for example, among the devices206-232 in the high-volume fulfillment center, by use of pallets. Thepallet sizing and pucking device 206 may configure pucks in a pallet. Apallet may be a transport structure for a number of prescriptioncontainers, and may include a number of cavities. A puck may be placedin one or more than one of the cavities in a pallet by the pallet sizingand pucking device 206. The puck may include a receptacle sized andshaped to receive a prescription container. Such containers may besupported by the pucks during carriage in the pallet. Different pucksmay have differently sized and shaped receptacles to accommodatecontainers of differing sizes, as may be appropriate for differentprescriptions.

The arrangement of pucks in a pallet may be determined by the orderprocessing device 114 based on prescriptions that the order processingdevice 114 decides to launch. The arrangement logic may be implementeddirectly in the pallet sizing and pucking device 206. Once aprescription is set to be launched, a puck suitable for the appropriatesize of container for that prescription may be positioned in a pallet bya robotic arm or pickers. The pallet sizing and pucking device 206 maylaunch a pallet once pucks have been configured in the pallet.

The loading device 208 may load prescription containers into the puckson a pallet by a robotic arm, a pick and place mechanism (also referredto as pickers), etc. In various implementations, the loading device 208has robotic arms or pickers to grasp a prescription container and moveit to and from a pallet or a puck. The loading device 208 may also printa label that is appropriate for a container that is to be loaded ontothe pallet, and apply the label to the container. The pallet may belocated on a conveyor assembly during these operations (e.g., at thehigh-volume fulfillment center, etc.).

The inspect device 210 may verify that containers in a pallet arecorrectly labeled and in the correct spot on the pallet. The inspectdevice 210 may scan the label on one or more containers on the pallet.Labels of containers may be scanned or imaged in full or in part by theinspect device 210. Such imaging may occur after the container has beenlifted out of its puck by a robotic arm, picker, etc., or may beotherwise scanned or imaged while retained in the puck. In someimplementations, images and/or video captured by the inspect device 210may be stored in the storage device 110 as order data 118.

The unit of use device 212 may temporarily store, monitor, label, and/ordispense unit of use products. In general, unit of use products areprescription drug products that may be delivered to a user or memberwithout being repackaged at the pharmacy. These products may includepills in a container, pills in a blister pack, inhalers, etc.Prescription drug products dispensed by the unit of use device 212 maybe packaged individually or collectively for shipping, or may be shippedin combination with other prescription drugs dispensed by other devicesin the high-volume fulfillment center.

At least some of the operations of the devices 206-232 may be directedby the order processing device 114. For example, the manual fulfillmentdevice 216, the review device 218, the automated dispensing device 214,and/or the packing device 226, etc. may receive instructions provided bythe order processing device 114.

The automated dispensing device 214 may include one or more devices thatdispense prescription drugs or pharmaceuticals into prescriptioncontainers in accordance with one or multiple prescription orders. Ingeneral, the automated dispensing device 214 may include mechanical andelectronic components with, in some implementations, software and/orlogic to facilitate pharmaceutical dispensing that would otherwise beperformed in a manual fashion by a pharmacist and/or pharmacisttechnician. For example, the automated dispensing device 214 may includehigh-volume fillers that fill a number of prescription drug types at arapid rate and blister pack machines that dispense and pack drugs into ablister pack. Prescription drugs dispensed by the automated dispensingdevices 214 may be packaged individually or collectively for shipping,or may be shipped in combination with other prescription drugs dispensedby other devices in the high-volume fulfillment center.

The manual fulfillment device 216 controls how prescriptions aremanually fulfilled. For example, the manual fulfillment device 216 mayreceive or obtain a container and enable fulfillment of the container bya pharmacist or pharmacy technician. In some implementations, the manualfulfillment device 216 provides the filled container to another devicein the pharmacy fulfillment devices 112 to be joined with othercontainers in a prescription order for a user or member.

In general, manual fulfillment may include operations at least partiallyperformed by a pharmacist or a pharmacy technician. For example, aperson may retrieve a supply of the prescribed drug, may make anobservation, may count out a prescribed quantity of drugs and place theminto a prescription container, etc. Some portions of the manualfulfillment process may be automated by use of a machine. For example,counting of capsules, tablets, or pills may be at least partiallyautomated (such as through use of a pill counter). Prescription drugsdispensed by the manual fulfillment device 216 may be packagedindividually or collectively for shipping, or may be shipped incombination with other prescription drugs dispensed by other devices inthe high-volume fulfillment center.

The review device 218 may process prescription containers to be reviewedby a pharmacist for proper pill count, exception handling, prescriptionverification, etc. Fulfilled prescriptions may be manually reviewedand/or verified by a pharmacist, as may be required by state or locallaw. A pharmacist or other licensed pharmacy person who may dispensecertain drugs in compliance with local and/or other laws may operate thereview device 218 and visually inspect a prescription container that hasbeen filled with a prescription drug. The pharmacist may review, verify,and/or evaluate drug quantity, drug strength, and/or drug interactionconcerns, or otherwise perform pharmacist services. The pharmacist mayalso handle containers which have been flagged as an exception, such ascontainers with unreadable labels, containers for which the associatedprescription order has been canceled, containers with defects, etc. Inan example, the manual review can be performed at a manual reviewstation.

The imaging device 220 may image containers once they have been filledwith pharmaceuticals. The imaging device 220 may measure a fill heightof the pharmaceuticals in the container based on the obtained image todetermine if the container is filled to the correct height given thetype of pharmaceutical and the number of pills in the prescription.Images of the pills in the container may also be obtained to detect thesize of the pills themselves and markings thereon. The images may betransmitted to the order processing device 114 and/or stored in thestorage device 110 as part of the order data 118.

The cap device 222 may be used to cap or otherwise seal a prescriptioncontainer. In some implementations, the cap device 222 may secure aprescription container with a type of cap in accordance with a userpreference (e.g., a preference regarding child resistance, etc.), a plansponsor preference, a prescriber preference, etc. The cap device 222 mayalso etch a message into the cap, although this process may be performedby a subsequent device in the high-volume fulfillment center.

The accumulation device 224 accumulates various containers ofprescription drugs in a prescription order. The accumulation device 224may accumulate prescription containers from various devices or areas ofthe pharmacy. For example, the accumulation device 224 may accumulateprescription containers from the unit of use device 212, the automateddispensing device 214, the manual fulfillment device 216, and the reviewdevice 218. The accumulation device 224 may be used to group theprescription containers prior to shipment to the member.

The literature device 228 prints, or otherwise generates, literature toinclude with each prescription drug order. The literature may be printedon multiple sheets of substrates, such as paper, coated paper, printablepolymers, or combinations of the above substrates. The literatureprinted by the literature device 228 may include information required toaccompany the prescription drugs included in a prescription order, otherinformation related to prescription drugs in the order, financialinformation associated with the order (for example, an invoice or anaccount statement), etc.

In some implementations, the literature device 228 folds or otherwiseprepares the literature for inclusion with a prescription drug order(e.g., in a shipping container). In other implementations, theliterature device 228 prints the literature and is separate from anotherdevice that prepares the printed literature for inclusion with aprescription order.

The packing device 226 packages the prescription order in preparationfor shipping the order. The packing device 226 may box, bag, orotherwise package the fulfilled prescription order for delivery. Thepacking device 226 may further place inserts (e.g., literature or otherpapers, etc.) into the packaging received from the literature device228. For example, bulk prescription orders may be shipped in a box,while other prescription orders may be shipped in a bag, which may be awrap seal bag.

The packing device 226 may label the box or bag with an address and arecipient's name. The label may be printed and affixed to the bag orbox, be printed directly onto the bag or box, or otherwise associatedwith the bag or box. The packing device 226 may sort the box or bag formailing in an efficient manner (e.g., sort by delivery address, etc.).The packing device 226 may include ice or temperature sensitive elementsfor prescriptions that are to be kept within a temperature range duringshipping (for example, this may be necessary in order to retainefficacy). The ultimate package may then be shipped through postal mail,through a mail order delivery service that ships via ground and/or air(e.g., UPS, FEDEX, or DHL, etc.), through a delivery service, through alocker box at a shipping site (e.g., AMAZON locker or a PO Box, etc.),or otherwise.

The unit of use packing device 230 packages a unit of use prescriptionorder in preparation for shipping the order. The unit of use packingdevice 230 may include manual scanning of containers to be bagged forshipping to verify each container in the order. In an exampleimplementation, the manual scanning may be performed at a manualscanning station. The pharmacy fulfillment device 112 may also include amail manifest device 232 to print mailing labels used by the packingdevice 226 and may print shipping manifests and packing lists.

While the pharmacy fulfillment device 112 in FIG. 2 is shown to includesingle devices 206-232, multiple devices may be used. When multipledevices are present, the multiple devices may be of the same device typeor models, or may be a different device type or model. The types ofdevices 206-232 shown in FIG. 2 are example devices. In otherconfigurations of the system 100, lesser, additional, or different typesof devices may be included.

Moreover, multiple devices may share processing and/or memory resources.The devices 206-232 may be located in the same area or in differentlocations. For example, the devices 206-232 may be located in a buildingor set of adjoining buildings. The devices 206-232 may be interconnected(such as by conveyors), networked, and/or otherwise in contact with oneanother or integrated with one another (e.g., at the high-volumefulfillment center, etc.). In addition, the functionality of a devicemay be split among a number of discrete devices and/or combined withother devices.

FIG. 3 illustrates the order processing device 114 according to anexample implementation. The order processing device 114 may be used byone or more operators to generate prescription orders, make routingdecisions, make prescription order consolidation decisions, trackliterature with the system 100, and/or view order status and other orderrelated information. For example, the prescription order may includeorder components.

The order processing device 114 may receive instructions to fulfill anorder without operator intervention. An order component may include aprescription drug fulfilled by use of a container through the system100. The order processing device 114 may include an order verificationsubsystem 302, an order control subsystem 304, and/or an order trackingsubsystem 306. Other subsystems may also be included in the orderprocessing device 114.

The order verification subsystem 302 may communicate with the benefitmanager device 102 to verify the eligibility of the member and reviewthe formulary to determine appropriate copayment, coinsurance, anddeductible for the prescription drug and/or perform a DUR (drugutilization review). Other communications between the order verificationsubsystem 302 and the benefit manager device 102 may be performed for avariety of purposes.

The order control subsystem 304 controls various movements of thecontainers and/or pallets along with various filling functions duringtheir progression through the system 100. In some implementations, theorder control subsystem 304 may identify the prescribed drug in one ormore than one prescription orders as capable of being fulfilled by theautomated dispensing device 214. The order control subsystem 304 maydetermine which prescriptions are to be launched and may determine thata pallet of automated-fill containers is to be launched.

The order control subsystem 304 may determine that an automated-fillprescription of a specific pharmaceutical is to be launched and mayexamine a queue of orders awaiting fulfillment for other prescriptionorders, which will be filled with the same pharmaceutical. The ordercontrol subsystem 304 may then launch orders with similar automated-fillpharmaceutical needs together in a pallet to the automated dispensingdevice 214. As the devices 206-232 may be interconnected by a system ofconveyors or other container movement systems, the order controlsubsystem 304 may control various conveyors: for example, to deliver thepallet from the loading device 208 to the manual fulfillment device 216from the literature device 228, paperwork as needed to fill theprescription.

The order tracking subsystem 306 may track a prescription order duringits progress toward fulfillment. The order tracking subsystem 306 maytrack, record, and/or update order history, order status, etc. The ordertracking subsystem 306 may store data locally (for example, in a memory)or as a portion of the order data 118 stored in the storage device 110.

FIG. 4 illustrates a functional block diagram of a machine learningsystem 400 that may be deployed within, or as a variation of, the systemof FIG. 1 . As shown, storage device 110 is in communication with thecomponents of machine learning system 400. Machine learning system 400includes a prescription processing system 410 which further includes aprocessor 411, a memory 412, an input/output 413, a communicationsdevice 414, a machine learning module 415, and a trained predictor 416.Exemplary attributes of components 411, 412, 413, and 414 are furtherdescribed in FIG. 5 with respect to exemplary corresponding components511, 512, 513, and 514, respectively.

Machine learning system 400 also includes a data warehouse 420 whichfurther includes a processor 421, a memory 422, an input/output 423, acommunications device 424, a set of historical orders 425, and a set ofpayer responses 426. Exemplary attributes of components 421, 422, 423,and 424 are further described in FIG. 5 with respect to exemplarycorresponding components 511, 512, 513, and 514, respectively. In someexamples, data warehouse is alternatively included within prescriptionprocessing system 410 or vice versa. In further examples, data warehouse420 and prescription processing system 410 are included within otherdevices including, for example, other devices 440.

Data warehouse 420 is substantially in communication (via any suitableform) with storage device 110 and the data elements order data 118,member data 120, claims data 122, drug data 124, prescription data 126,and plan sponsor data 128. Further, via at least network 430, allcomponents of machine learning system 400 are capable of retrieving datafrom and writing data to storage device 110. As indicated and describedherein, historical orders 425 and payer responses 426 relate to datawithin storage device 110 including at least order data 118, member data120, claims data 126, and plan sponsor data 128. Moreover, datareflected in machine learning system 400 including production orders 441may be retrieved from, or be substantially represented in, storagedevice 110. In at least some examples, machine learning system 400creates or retrieves necessary data from storage device 110.

Machine learning system 400 also may include other devices 440 thatallow access any system of machine learning system 400 directly orindirectly. In the example embodiment, other devices 440 includes ordersystems that are capable of providing and transmitting currentprescription orders to the machine learning system 400. Machine learningsystem 400 further includes a network 430 that is configured to providecommunication between the systems 410, 420, and 440 of machine learningsystem 400, and from and to external devices (not shown).

In the example embodiment, prescription processing system 410 receives afirst portion of the historical orders 425 and a first portion of thepayer responses 426 from data warehouse 420. Prescription processingsystem 410 further applies at least one data balancing operation to thefirst portion of the historical orders 425 and the first portion of thepayer responses 426.

In some examples, prescription processing system 410 is configured tofurther pre-process data before training the trained predictor 416 byre-balancing the first portion of the historical orders 425 and thefirst portion of the payer responses 426.

Prescription processing system 410 further generates a data model of thefirst portion of the historical orders 425 and the first portion of thepayer responses 426, wherein the data model is substantially representedby a tree-structure including one or more leaves. Prescriptionprocessing system 410 applies a predictive machine learning model 415 tothe data model to generate a trained predictor of whether a medicalorder requires PA 416 (“trained predictor”) associated with order data425 and 426.

In the example embodiment, prescription processing system 410 appliesthe predictive machine learning model 415 to the data model using acombination of algorithms. In the exemplary embodiment, the combinationof algorithms includes at least one of a) a nearest neighbor algorithm,b) a logistic regression algorithm, c) a random forest algorithm, and d)a naïve Bayesian algorithm. In at least some embodiments, when thepredictive machine learning model 415 is applied, the nearest neighboralgorithm is configured to consider a set of neighbors including thenearest twenty neighbors identified for each of the leaves of thetree-structure. In some example embodiments, the combination ofalgorithms includes any combination or permutation including a) anearest neighbor algorithm, b) a logistic regression algorithm, c) arandom forest algorithm, and/or d) a naïve Bayesian algorithm.

In some embodiments, prescription processing system 410 is configured toalso receive a second portion of the historical orders 425 and a secondportion of the payer responses 426. In such embodiments, prescriptionprocessing system 410 is also configured to apply the trained predictor416 to the second portion of the historical orders 425 to determinewhether a medical order requires PA for each of the second portion ofthe historical orders 425. Prescription processing system 410 alsoreceives a pre-defined threshold for error and determines an error ratefor the trained predictor 416 by comparing the determined PA requirementfor each of the second portion of the historical orders 425 to theassociated second portion of the payer responses 426. Based on thedetermined error rate and the pre-defined threshold for error,prescription processing system 410 determines whether the trainedpredictor 416 requires re-training.

When prescription processing system 410 determines that trainedpredictor 416 requires retraining, prescription processing system 410 isconfigured to perform such retraining. Prescription processing system410 accomplishes this by generating a second data model of the secondportion of the historical orders 425 and the second portion of the payerresponses 426. The second data model is substantially represented by atree-structure including one or more leaves. Prescription processingsystem 410 applies a predictive machine learning model to the seconddata model to generate a re-trained predictor of whether a medical orderrequires PA associated with production orders 441. The re-trainedpredictor replaces trained predictor 416. Prescription processing system410 receives a third portion of the historical orders 425 and a thirdportion of the payer responses 426 and applies the re-trained predictor(now trained predictor 416) to the third portion of the historicalorders 425 to determine whether a medical order requires PA for each ofthe third portion of the historical orders 425. Prescription processingsystem 410 receives the pre-defined threshold for error and determines asecond error rate for the re-trained predictor (now trained predictor416) by comparing the determined PA requirement for each of the thirdportion of the historical orders 425 to the associated third portion ofthe payer responses 426.

In additional examples, prescription processing system 410 is configuredto receive additional historical orders 425 and additional historicalpayer responses 426 and iteratively re-train the trained predictor 416on the additional historical orders 425 and the additional historicalpayer responses 426 in parallel with the use of the trained predictor416. This approach allows the system to utilize the benefits of thetrained predictor 416 while actively updating it to ensure that changesin external conditions are timely captured and used for re-training. Inthis way, the prescription processing system 410 can ensure that thedata models and trained predictor 416 do not become static or stale, andinstead reflect changes in the relationship between medical order datasets and whether a medical order requires PA.

Once trained predictor 416 is trained (or retrained, as needed) toaccurately predict whether a medical order requires PA, prescriptionprocessing system 410 receives one or more production orders 441 from anorder system such as other devices 440. Prescription processing system410 is configured to apply the trained predictor 416 to the productionorders to determine whether a medical order requires PA for each of theproduction orders 441 process the production orders with each associatedPA requirement determination. As a result of this step, prescriptionprocessing system 410 can ensure that an accurate determination ofwhether a medical order requires PA can be determined for each receivedproduction order 441.

FIG. 5 is a functional block diagram of an example computing device 500that may be used in the system architecture of FIG. 4 . Specifically,computing device 500 illustrates an exemplary configuration of acomputing device such as prescription processing system 410, datawarehouse 420, or other devices 440. Computing device 500 illustrates anexemplary configuration of a computing device operated by a user 501 inaccordance with one embodiment of the present invention. Computingdevice 500 may include, but is not limited to, prescription processingsystem 410, data warehouse 420, other devices 440, other user systems,and other server systems. Computing device 500 may also include pharmacydevices 106 including pharmacy fulfillment devices 112, order processingdevices 114, and pharmacy management devices 116, storage devices 110,benefit manager devices 102, and user devices 108 (all shown in FIG. 1), mobile computing devices, stationary computing devices, computingperipheral devices, smart phones, wearable computing devices, medicalcomputing devices, and vehicular computing devices. Alternatively,computing device 500 may be any computing device capable of the machinelearning methods for predicting whether a medical order requires PAdescribed herein. In some variations, the characteristics of thedescribed components may be more or less advanced, primitive, ornon-functional.

In the exemplary embodiment, computing device 500 includes a processor511 for executing instructions. In some embodiments, executableinstructions are stored in a memory area 512. Processor 511 may includeone or more processing units, for example, a multi-core configuration.Memory area 512 is any device allowing information such as executableinstructions and/or written works to be stored and retrieved. Memoryarea 512 may include one or more computer readable media.

Computing device 500 also includes at least one input/output component513 for receiving information from and providing information to user501. In some examples, input/output component 513 may be of limitedfunctionality or non-functional as in the case of some wearablecomputing devices. In other examples, input/output component 513 is anycomponent capable of conveying information to or receiving informationfrom user 501. In some embodiments, input/output component 513 includesan output adapter such as a video adapter and/or an audio adapter.Input/output component 513 may alternatively include an output devicesuch as a display device, a liquid crystal display (LCD), organic lightemitting diode (OLED) display, or “electronic ink” display, or an audiooutput device, a speaker or headphones. Input/output component 513 mayalso include any devices, modules, or structures for receiving inputfrom user 501. Input/output component 513 may therefore include, forexample, a keyboard, a pointing device, a mouse, a stylus, a touchsensitive panel, a touch pad, a touch screen, a gyroscope, anaccelerometer, a position detector, or an audio input device. A singlecomponent such as a touch screen may function as both an output andinput device of input/output component 513. Input/output component 513may further include multiple sub-components for carrying out input andoutput functions.

Computing device 500 may also include a communications interface 514,which may be communicatively coupleable to a remote device such as aremote computing device, a remote server, or any other suitable system.Communication interface 514 may include, for example, a wired orwireless network adapter or a wireless data transceiver for use with amobile phone network, Global System for Mobile communications (GSM), 3G,4G, or other mobile data network or Worldwide Interoperability forMicrowave Access (WIMAX). Communications interface 514 is configured toallow computing device 500 to interface with any other computing deviceor network using an appropriate wireless or wired communicationsprotocol such as, without limitation, BLUETOOTH®, Ethernet, or IEE802.11. Communications interface 514 allows computing device 500 tocommunicate with any other computing devices with which it is incommunication or connection.

FIG. 6 is a flow diagram 600 representing the analytics process from theperspective of the prescription processing system 410 or prescriptionprocessing computing device shown in FIG. 4 . Flow diagram 600 depictsthe exemplary steps that are executed by prescription processing system410 to train trained predictor 416 (shown in FIG. 4 ) and predict the PAstates of production orders 441 (shown in FIG. 4 ).

In the example embodiment, prescription processing system 410 receives610 a first portion of the historical orders 425 and a first portion ofthe payer responses 426 from data warehouse 420 (all shown in FIG. 4 ).Prescription processing system 410 applies 620 at least one databalancing operation to the first portion of the historical orders 425and the first portion of the payer responses 426. Prescriptionprocessing system 410 generates 630 a data model of the first portion ofthe historical orders 425 and the first portion of the payer responses426, wherein the data model is substantially represented by atree-structure including one or more leaves.

Prescription processing system 410 also applies 640 a predictive machinelearning model to the data model to generate a trained predictor 416(shown in FIG. 4 ) of whether a medical order requires PA associatedwith production order data 441. Prescription processing system 410 alsoreceives 650 one or more production orders 441 including production datafor prescription orders that may not include information regardingwhether a medical order requires PA.

Prescription processing system 410 applies 660 trained predictor 416 tothe production orders 441 to determine whether a medical order requiresPA for each of the production orders 441. Prescription processing system410 processes 670 the production orders 441 with each associated PAdetermination as determined by trained predictor 416.

FIG. 7 is a diagram of elements of one or more example computing devicesthat may be used in the system shown in FIGS. 1 and 4 . Specifically,FIG. 7 describes subsystems available to machine learning system 400that are capable of providing the functionality described herein.Machine learning system 400 includes historical order processingsubsystem 702 that is configured to receive, process, manage, andtransport historical orders 425 within machine learning system 400 andto prescription processing system 410. Machine learning system 400includes payer response processing subsystem 704 that is configured toreceive, process, manage, and transport payer responses 426 withinmachine learning system 400 and to prescription processing system 410.Using historical order processing subsystem 702 and payer responseprocessing subsystem 704, machine learning system 400 is configured tomaintain relationships between entries and records for historical orders425 and payer responses 426 within data warehouse 420. Historical orderprocessing subsystem 702 and payer response processing subsystem 704 arealso configured to provide a first portion of the historical orders 425and a first portion of the payer responses 426 to prescriptionprocessing system 410.

Machine learning system 400 also includes data model generationsubsystem 706. Data model generation subsystem 706 is configured to atleast generate data models of the first portion of the historical orders425 and the first portion of the payer responses 426, wherein the datamodel is substantially represented by a tree-structure including one ormore leaves. Data model generation subsystem 706 is also configured togenerate a second data model of the second portion of the historicalorders 425 and the second portion of the payer responses 426, whereinthe second data model is substantially represented by a tree-structureincluding one or more leaves.

Machine learning system 400 also includes training subsystem 708 whichis configured to at least apply a predictive machine learning model tothe data model to generate a trained predictor of whether a medicalorder requires PA associated with order data. Training subsystem 708substantially represents machine learning module 415. Training subsystem708 is also configured to apply the predictive machine learning model tothe data model (generated by data model generation subsystem 706). Thetraining subsystem 708 may train the predictive machine learning modelusing a composite algorithm of a) a nearest neighbor algorithm, b) alogistic regression algorithm, c) a random forest algorithm, and d) anaïve Bayesian algorithm. In some examples, the training subsystem 708configures the nearest neighbor algorithm to consider a set of neighborsincluding the nearest twenty neighbors identified for each of the leavesof the tree-structure.

Machine learning system 400 also includes verification subsystem 710 todetermine whether the trained predictor 416 is successfully trained. Assuch, the verification subsystem 710 is configured to verify the trainedpredictor 416. As such, verification subsystem 710 receives a secondportion of the historical orders 425 and a second portion of the payerresponses 426 and applies the trained predictor 416 to the secondportion of the historical orders 425 to determine whether a medicalorder requires PA for each of the second portion of the historicalorders. Verification subsystem 710 also receives or otherwise determinesa pre-defined threshold for error. The pre-defined threshold includesthe threshold level of performance required for trained predictor 416.Verification subsystem 710 also determines an error rate for the trainedpredictor 416 by comparing the determined PA requirement for each of thesecond portion of the historical orders 425 to the associated secondportion of the payer responses 426 and determines whether the trainedpredictor 416 requires re-training. Verification subsystem 710determines whether the trained predictor 416 requires re-training bycomparing the determined error rate to the pre-defined threshold.

If verification subsystem 710 determines that trained predictor 416requires retraining, verification subsystem 710 is also configured tore-train the trained predictor by generating a second data model (usingdata model generation subsystem 706) of the second portion of thehistorical orders 425 and the second portion of the payer responses 426.The second data model is substantially represented by a tree-structureincluding one or more leaves. Verification subsystem 710 also causestraining subsystem 708 to apply the predictive machine learning model tothe second data model to generate a re-trained predictor of whether amedical order requires PA associated with order data 441. Verificationsubsystem 710 is also configured to receive a third portion of thehistorical orders 425 and a third portion of the payer responses 426 andapply the re-trained predictor 416 to the third portion of thehistorical orders 425 to determine whether a medical order requires PAfor each of the third portion of the historical orders 425. After suchre-training, verification subsystem 710 is configured to receive thepre-defined threshold for error and determine a second error rate forthe re-trained predictor 416 by comparing the determined PA requirementfor each of the third portion of the historical orders 425 to theassociated third portion of the payer responses 426. As such,verification subsystem 710 allows for dynamic updating and re-trainingof trained predictor 416 to ensure accuracy of the prediction of whethera medical order requires PA.

Machine learning system 400 also includes a processing subsystem 712which is configured to receive one or more production orders 441, applythe trained predictor 416 to the production orders 441 to determinewhether a medical order requires PA for each of the production orders441, and process the production orders 441 with each associated PArequirement determination.

Machine learning system 400 also includes pre-processing servicesincluded within rebalancing and processing subsystem 714. Rebalancingand processing subsystem 714 is configured to perform data balancingfunctions including, for example, applying at least one data balancingoperation to the first portion of the historical orders and the firstportion of the payer responses. Rebalancing and processing subsystem 714is also configured to re-balance the first portion of the historicalorders 425 and the first portion of the payer responses 426.

The foregoing description is merely illustrative in nature and is in noway intended to limit the disclosure, its application, or uses. Thebroad teachings of the disclosure can be implemented in a variety offorms. Therefore, while this disclosure includes particular examples,the true scope of the disclosure should not be so limited since othermodifications will become apparent upon a study of the drawings, thespecification, and the following claims. It should be understood thatone or more steps within a method may be executed in different order (orconcurrently) without altering the principles of the present disclosure.Further, although each of the embodiments is described above as havingcertain features, any one or more of those features described withrespect to any embodiment of the disclosure can be implemented in and/orcombined with features of any of the other embodiments, even if thatcombination is not explicitly described. In other words, the describedembodiments are not mutually exclusive, and permutations of one or moreembodiments with one another remain within the scope of this disclosure.

Spatial and functional relationships between elements (for example,between modules) are described using various terms, including“connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitlydescribed as being “direct,” when a relationship between first andsecond elements is described in the above disclosure, that relationshipencompasses a direct relationship where no other intervening elementsare present between the first and second elements, and also an indirectrelationship where one or more intervening elements are present (eitherspatially or functionally) between the first and second elements. Asused herein, the phrase at least one of A, B, and C should be construedto mean a logical (A OR B OR C), using a non-exclusive logical OR, andshould not be construed to mean “at least one of A, at least one of B,and at least one of C.”

In the figures, the direction of an arrow, as indicated by thearrowhead, generally demonstrates the flow of information (such as dataor instructions) that is of interest to the illustration. For example,when element A and element B exchange a variety of information butinformation transmitted from element A to element B is relevant to theillustration, the arrow may point from element A to element B. Thisunidirectional arrow does not imply that no other information istransmitted from element B to element A. Further, for information sentfrom element A to element B, element B may send requests for, or receiptacknowledgements of, the information to element A. The term subset doesnot necessarily require a proper subset. In other words, a first subsetof a first set may be coextensive with (equal to) the first set.

In this application, including the definitions below, the term “module”or the term “controller” may be replaced with the term “circuit.” Theterm “module” may refer to, be part of, or include processor hardware(shared, dedicated, or group) that executes code and memory hardware(shared, dedicated, or group) that stores code executed by the processorhardware.

The module may include one or more interface circuits. In some examples,the interface circuit(s) may implement wired or wireless interfaces thatconnect to a local area network (LAN) or a wireless personal areanetwork (WPAN). Examples of a LAN are Institute of Electrical andElectronics Engineers (IEEE) Standard 802.11-2016 (also known as theWIFI wireless networking standard) and IEEE Standard 802.3-2015 (alsoknown as the ETHERNET wired networking standard). Examples of a WPAN arethe BLUETOOTH wireless networking standard from the Bluetooth SpecialInterest Group and IEEE Standard 802.15.4.

The module may communicate with other modules using the interfacecircuit(s). Although the module may be depicted in the presentdisclosure as logically communicating directly with other modules, invarious implementations the module may actually communicate via acommunications system. The communications system includes physicaland/or virtual networking equipment such as hubs, switches, routers, andgateways. In some implementations, the communications system connects toor traverses a wide area network (WAN) such as the Internet. Forexample, the communications system may include multiple LANs connectedto each other over the Internet or point-to-point leased lines usingtechnologies including Multiprotocol Label Switching (MPLS) and virtualprivate networks (VPNs).

In various implementations, the functionality of the module may bedistributed among multiple modules that are connected via thecommunications system. For example, multiple modules may implement thesame functionality distributed by a load balancing system. In a furtherexample, the functionality of the module may be split between a server(also known as remote, or cloud) module and a client (or, user) module.

The term code, as used above, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes, datastructures, and/or objects. Shared processor hardware encompasses asingle microprocessor that executes some or all code from multiplemodules. Group processor hardware encompasses a microprocessor that, incombination with additional microprocessors, executes some or all codefrom one or more modules. References to multiple microprocessorsencompass multiple microprocessors on discrete dies, multiplemicroprocessors on a single die, multiple cores of a singlemicroprocessor, multiple threads of a single microprocessor, or acombination of the above.

Shared memory hardware encompasses a single memory device that storessome or all code from multiple modules. Group memory hardwareencompasses a memory device that, in combination with other memorydevices, stores some or all code from one or more modules.

The term memory hardware is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave). The term computer-readablemedium is therefore considered tangible and non-transitory. Non-limitingexamples of a non-transitory computer-readable medium are nonvolatilememory devices (such as a flash memory device, an erasable programmableread-only memory device, or a mask read-only memory device), volatilememory devices (such as a static random access memory device or adynamic random access memory device), magnetic storage media (such as ananalog or digital magnetic tape or a hard disk drive), and opticalstorage media (such as a CD, a DVD, or a Blu-ray Disc).

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks andflowchart elements described above serve as software specifications,which can be translated into the computer programs by the routine workof a skilled technician or programmer.

The computer programs include processor-executable instructions that arestored on at least one non-transitory computer-readable medium. Thecomputer programs may also include or rely on stored data. The computerprograms may encompass a basic input/output system (BIOS) that interactswith hardware of the special purpose computer, device drivers thatinteract with particular devices of the special purpose computer, one ormore operating systems, user applications, background services,background applications, etc.

The computer programs may include: (i) descriptive text to be parsed,such as HTML (hypertext markup language), XML (extensible markuplanguage), or JSON (JavaScript Object Notation), (ii) assembly code,(iii) object code generated from source code by a compiler, (iv) sourcecode for execution by an interpreter, (v) source code for compilationand execution by a just-in-time compiler, etc. As examples only, sourcecode may be written using syntax from languages including C, C++, C#,Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl,Pascal, Curl, OCaml, Javascript®, HTML5 (Hypertext Markup Language 5threvision), Ada, ASP (Active Server Pages), PHP (PHP: HypertextPreprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, VisualBasic®, Lua, MATLAB, SIMULINK, and Python®.

What is claimed is:
 1. A system for determining prior authorization (PA)requirements for prescriptions, the system comprising: at least oneprocessor; and at least one memory component having instructions storedthereon, which, when executed by the at least one processor, cause theat least one processor to perform operations comprising: receiving afirst portion of a plurality of historical orders and a first portion ofa plurality of payer responses, the plurality of historical orders beingassociated with respective ones of the plurality of payer responses;applying at least one data balancing operation to the first portion ofthe plurality of historical orders and the first portion of the payerresponses; generating a data model of the first portion of the pluralityof historical orders and the first portion of the payer responses, thedata model being represented by a tree-structure including a pluralityof leaves, nodes, and edges; generating a predictive machine learningmodel; applying the predictive machine learning model to the data modelto generate a trained predictor of whether a medical order requiresprior authorization (PA), associated with order data; receiving aplurality of prescription production orders; applying the trainedpredictor to the plurality of prescription production orders todetermine whether the medical order requires a PA for respective ones ofthe plurality of prescription production orders; and processing theplurality of prescription production orders with associated PArequirement determinations for the respective ones.
 2. The system ofclaim 1, wherein the operations further comprise: updating the trainedpredictor, by: applying the predictive machine learning model to thedata model; and training the predictive machine learning model using atleast one of a) a k-nearest neighbor algorithm, b) a logistic regressionalgorithm, c) a random forest algorithm, and d) a naive Bayesianalgorithm.
 3. The system of claim 2, wherein training the predictivemachine learning model further comprises: applying the k-nearestneighbor algorithm, including considering a set of neighbors comprisingthe nearest twenty neighbors identified for the plurality of leaves ofthe tree-structure.
 4. The system of claim 1, wherein the operationsfurther comprise: receiving a second portion of the plurality ofhistorical orders and a second portion of the plurality of payerresponses; applying the trained predictor to the second portion of theplurality of historical orders to determine whether a medical orderrequires PA for the second portion of the plurality of historicalorders; receiving a pre-defined threshold for error; determining anerror rate for the trained predictor by comparing the determined PArequirement for the second portion of the plurality of historical ordersto the associated second portion of the plurality of payer responses;and determining whether the trained predictor requires re-training. 5.The system of claim 4, wherein the operations further comprise: upondetermining that the trained predictor requires re-training, re-trainingthe trained predictor by generating a second data model of the secondportion of the plurality of historical orders and the second portion ofthe payer responses, the second data model being represented by atree-structure including a plurality of leaves, and applying thepredictive machine learning model to the second data model to generate are-trained predictor of whether a medical order requires PA, associatedwith order data; receiving a third portion of the plurality ofhistorical orders and a third portion of the plurality of payerresponses; applying the re-trained predictor to the third portion of theplurality of historical orders to determine whether a medical orderrequires PA for respective ones of the third portion of the plurality ofhistorical orders; receiving the pre-defined threshold for error; anddetermining a second error rate for the re-trained predictor bycomparing the determined PA requirement for the respective ones of thethird portion of the plurality of historical orders to the associatedthird portion of the plurality of payer responses.
 6. The system ofclaim 1, wherein the operations further comprise: re-balancing the firstportion of the plurality of historical orders and the first portion ofthe plurality of payer responses, to create balanced data; andgenerating the data model based on the balanced data.
 7. The system ofclaim 1, wherein the operations further comprise: receiving additionalpluralities of historical orders and additional pluralities ofhistorical payer responses; and iteratively re-training the trainedpredictor on the additional pluralities of historical orders and theadditional pluralities of historical payer responses in parallel withapplying the trained predictor to the plurality of prescriptionproduction orders.
 8. A method for determining prior authorization (PA)requirements for prescriptions, the method comprising: receiving a firstportion of a plurality of historical orders and a first portion of aplurality of payer responses, the plurality of historical orders beingassociated with respective ones of the plurality of payer responses;applying at least one data balancing operation to the first portion ofthe plurality of historical orders and the first portion of the payerresponses; generating a data model of the first portion of the pluralityof historical orders and the first portion of the payer responses, thedata model being represented by a tree-structure including a pluralityof leaves, nodes, and edges; generating a predictive machine learningmodel; applying the predictive machine learning model to the data modelto generate a trained predictor of whether a medical order requiresprior authorization (“PA”) associated with order data; receiving aplurality of prescription production orders; applying the trainedpredictor to the plurality of prescription production orders todetermine whether the medical order requires PA for respective ones ofthe plurality of prescription production orders; and processing theplurality of prescription production orders with associated PArequirement determinations for the respective ones.
 9. The method ofclaim 8, further comprising: applying the predictive machine learningmodel to the data model; and training the predictive machine learningmodel using at least one of a) a k-nearest neighbor algorithm, b) alogistic regression algorithm, c) a random forest algorithm, and d) anaive Bayesian algorithm.
 10. The method of claim 9, further comprising:using the k-nearest neighbor algorithm that is configured to consider aset of neighbors comprising a nearest twenty neighbors identified forrespective ones of the plurality of leaves of the tree-structure. 11.The method of claim 8, further comprising: receiving a second portion ofthe plurality of historical orders and a second portion of the pluralityof payer responses; applying the trained predictor to the second portionof the plurality of historical orders to determine whether a medicalorder requires PA for respective ones of the second portion of theplurality of historical orders; receiving a pre-defined threshold forerror; determining an error rate for the trained predictor by comparingthe determined PA requirement for the respective ones of the secondportion of the plurality of historical orders to the associated secondportion of the plurality of payer responses; and determining whether thetrained predictor requires re-training.
 12. The method of claim 11,further comprising: upon determining that the trained predictor requiresre-training, re-training the trained predictor by generating a seconddata model of the second portion of the plurality of historical ordersand the second portion of the payer responses, the second data modelbeing represented by a tree-structure including a plurality of leaves,and applying a predictive machine learning model to the second datamodel to generate a re-trained predictor of whether a medical orderrequires PA, associated with order data; receiving a third portion ofthe plurality of historical orders and a third portion of the pluralityof payer responses; applying the re-trained predictor to the thirdportion of the plurality of historical orders to determine whether themedical order requires PA for respective ones of the third portion ofthe plurality of historical orders; receiving the pre-defined thresholdfor error; and determining a second error rate for the re-trainedpredictor by comparing the determined PA requirement for the respectiveones of the third portion of the plurality of historical orders to theassociated third portion of the plurality of payer responses.
 13. Themethod of claim 8, further comprising re-balancing the first portion ofthe plurality of historical orders and the first portion of theplurality of payer responses.
 14. The method of claim 8, furthercomprising: receiving additional pluralities of historical orders andadditional pluralities of historical payer responses; and iterativelyre-training the trained predictor on the additional pluralities ofhistorical orders and the additional pluralities of historical payerresponses in parallel with the use of the trained predictor.
 15. Amethod for determining prior authorization (PA) requirements forprescriptions, the method comprising: generating a data model of a firstportion of a plurality of historical orders and a first portion of aplurality of payer responses, the plurality of historical orders beingassociated with respective ones of the plurality of payer responses, andthe data model being represented by a tree-structure including aplurality of leaves, nodes, and edges; generating a predictive machinelearning model; iteratively training and re-training a predictor, basedon the data model and the predictive machine learning model, to generatea non-static trained predictor; applying the non-static trainedpredictor to a plurality of prescription production orders to determinerequired prior authorizations (PAs) for the plurality of prescriptionproduction orders; and processing the plurality of prescriptionproduction orders with the required PAs for the plurality ofprescription production orders.
 16. The method of claim 15, whereiniteratively training and re-training the non-static trained predictor,further comprises: during a first iteration, applying the predictivemachine learning model to the data model to generate an initial trainedpredictor of whether one of the plurality of prescription productionorders requires a PA, the non-static trained predictor comprising theinitial trained predictor; generating a second data model of updateddata comprising a second portion of the plurality of historical ordersand a second portion of the plurality of payer responses; during asecond iteration, applying the predictive machine learning model to thesecond data model to generate an updated re-trained predictor based onthe updated data; and updating the non-static trained predictor byreplacing the initial trained predictor with the updated re-trainedpredictor.
 17. The method of claim 15, further comprising: ensuringnon-static conditions for the predictor and the data model, by: updatingthe predictor via timely capture of changes in external conditions; anditeratively re-training the predictor, based on the changes, in parallelwith applying the non-static trained predictor to the plurality ofprescription production orders.
 18. The method of claim 15, furthercomprising: prior to generating the data model, applying at least onedata balancing operation to the first portion of the plurality ofhistorical orders and the first portion of the payer responses, tocreate balanced data; and generating the data model using the balanceddata.
 19. The method of claim 15, further comprising: establishing acommunication connection to a data warehouse system, the data warehousesystem including the plurality of historical orders and the plurality ofpayer responses, the plurality of historical orders being associatedwith respective ones of the plurality of payer responses; receiving thefirst portion of the plurality of historical orders and the firstportion of the plurality of payer responses, via the communicationconnection, to create a set of received data; and generating the datamodel based on the set of received data.
 20. The method of claim 15,further comprising: updating the predictor, by: applying the predictivemachine learning model to the data model; and training or re-trainingthe predictive machine learning model using at least one of a) ak-nearest neighbor algorithm, b) a logistic regression algorithm, c) arandom forest algorithm, and d) a naive Bayesian algorithm.